System and methods for real-time delivery of specialized telecommunications services

ABSTRACT

A system, method and computer readable storage medium are disclosed to provide dynamic network resource management in a telecommunications network where at least a portion of the network resources are located at an edge of the telecommunications network. The capacity available, the aggregate demand of resources at a given time, and the cost of the network resource demanded as well as each customers upper price limit may be considered and adjusted in real-time by different network resource allocation models.

FIELD OF THE DISCLOSURE

The present disclosure relates to a dynamic, real-time resourceallocation and pricing model directed to third parties using edgecomputing resources in a telecommunications network.

BACKGROUND

In recent years computer networks have seen the growth of centralizedcloud computing and also the growth of Internet of Things (IoT) devices.IoT devices have proliferated in a variety of settings to provideindustrial, scientific, medical, agricultural, infrastructure,communication, consumer, and other types of data. Some IoT devicesmeasure physical or network conditions at their locations, while otherIoT devices receive commands and implement local control functions(e.g., allowing remote operation or optimization of industrial processesor smart devices). Many of these applications require special servicesof a telecommunications network. Many IOT devices, such as those foundin airplanes, drones, automobiles, or public safety devices have verydemanding latency requirements. Commercial airplanes, for example, havedemanding latency requirements and, in addition, generate large amountsof data. This data far exceeds what can be transmitted by the airplaneover a satellite link to a centralized cloud computing system. Latencydemands by airplanes or by autonomous vehicles, such as may be used tocontrol steering or braking, is of paramount importance and thoselatency demands may not be met by a remotely located cloud network.Security and privacy concerns are also growing, such as those arisingfrom the growth of home security systems and home automation devicestied to cloud storage devices. User privacy is at risk when data fromhome cameras or automation devices is transmitted back and forth fromthe home to cloud storage devices. These and other use cases have led tothe rise of edge computing architecture models, which allowscomputations, storage and other tasks to be performed at the edge of thenetwork, closer to where the data is generated, in order to address thechallenges of latency, bandwidth, security or other concerns. Edgecomputing resources, however, mean that resources are distributedthroughout the network and may be in adequate supply in the aggregate,but be in short supply at any given location, thus creating a need formore careful resource allocation.

SUMMARY

Different use cases in an edge architecture have a wide range oflatency, bandwidth and reliability requirements, with different usesrequiring different priorities. Some uses require the lowest possiblelatency, while other applications require a low cost or high security.The emergence of the edge computing architecture and associated usestherefore requires an optimized method for controlling and charging forsuch diverse services where data is processed closer to the end-userbased upon a variety of demands.

The innovation described herein uses automatic resource management toefficiently manage resources of the telecommunications operator. Itincludes a dynamic, real-time pricing model similar to a double auctionmodel. The model is directed to third party partners (also referred toas “customers” herein) using edge-computing resources, such as for aspecialized service on a telecommunications network. The use of anautomated model to efficiently manage available network resources avoidsthe need to manually vet and evaluate different use cases for concern ofoverloading the network. This innovation further allows for automaticdetermination of appropriate rents for partners of the telecommunicationoperator. This could, for example be deployed on a wireless carrier's 5Ginfrastructure to manage use of network resources, though the innovationis not limited to a 5G network. In the edge computing systemarchitecture, resources of the telecommunication network operator, suchas processing units, memory, storage, routing, switching or securityservices are located at or near the source of the data, such as at thecell site, rather than relying on resources located in a centralrepository. Edge computing resources, however, mean that resources aredistributed throughout the network and may be in short supply at anygiven location. With an edge architecture, then, it is very possible foran operator to have adequate resources which are deployedinappropriately at the wrong edge of the network, especially in light ofchanging demand.

Careful and more sophisticated management of these resources istherefore needed and different pricing for these resources is a needwhich is addressed by a simultaneous double auction model used in thisinnovation. The model performs automatic resource management, revenuemaximization as well as cost minimization for the network operator. Thismodel for resources assumes that demand is dynamic and varies in realtime. The supply may also vary based on time of day or type andaggregated quantity of resource demanded. The model also considers theparticular network resources that are required (e.g., CPU, GraphicsProcessing Unit (GPU), storage, security services, etc.), and may alsoconsider: the resources required, the cost to provide the resource, thehistory of customer requests or the history of resource usage and thecustomer requested duration of need.

According to an aspect of the techniques described herein, a method forproviding dynamic network resource management in a telecommunicationsnetwork in which at least a portion of the network resources are locatedat an edge of the telecommunications network may include: obtainingprice parameter data in a plurality of customer service level agreementsassociated with a plurality of customers; determining levels of networkresources demanded by each of the plurality of customers based uponcurrent network resource use; generating an aggregate network resourcedemand function based upon the price parameter data and the levels ofnetwork resources demanded; generating a network resource supplyfunction based upon availability of one or more network resources in thetelecommunications network; determining an allocation of the networkresources based upon the aggregate network resource demand function andthe network resource supply function; controlling the network resourcesto provide services to the plurality of customers according to theallocation; and/or adjusting the allocation in response to changes tocurrent network resource use or changes to the availability of thenetwork resources. The aggregate network resource demand function mayindicate types of network resources demanded and locations associatedwith the demand, and the network resource supply function may indicatethe types of the network resources and locations associated with thenetwork resources.

Systems or non-transitory computer-readable storage medium storingexecutable instructions for implementing all or part of the methodsdescribed may also be provided in some aspects. Such systems orcomputer-readable media may include executable instructions to cause oneor more processors to implement part or all of the methods described.Additional or alternative features described herein below may beincluded in some aspects.

BRIEF DESCRIPTION OF THE DRAWINGS

The figures described below depict various aspects of the applications,methods, and systems disclosed herein. Each figure depicts an embodimentof a particular aspect of the disclosed applications, systems andmethods, and that each of the Figures is intended to accord with one ormore possible embodiments thereof. Furthermore, wherever possible, thefollowing description refers to the reference numerals included in thefollowing figures, in which features depicted in multiple figures aredesignated with consistent reference numerals.

FIG. 1 illustrates a block diagram showing the use of a serviceconductor component in a wireless edge network.

FIG. 2 illustrates a block diagram showing the use of a serviceconductor attached to the components of 3GPP 5G wireless network.

FIG. 3 illustrates a block diagram of an exemplary service conductor,including different modes of a dynamic pricing model contained in thememory of the service conductor.

FIG. 4 illustrates a flow diagram of an exemplary method to implementthe dynamic real-time model.

DETAILED DESCRIPTION

Edge computing resources can be managed by the systems, methods anddevices described herein. FIG. 1 illustrates a block diagram showing anexemplary service conductor 130 configured for use in managing edgeresources of a wireless telecommunications network. The serviceconductor 130 and its subcomponents allocate and track resources acrossthe network in real-time. The service conductor 130 also distributesservice requests to available resources across the network based oncustomer service level agreements (SLAs) or based upon customer input innear real-time. In addition, the service conductor 130 measures andmanages compliance with the SLAs.

The service conductor 130 is connected to various components of thenetwork via one or more router devices. Each of the router devices 122,124, 126, 128 (which may be referred to herein as a “router”) may be arouter, a server that also performs a routing function, a data switch ora data hub. In the exemplary embodiment illustrated in FIG. 1, theservice conductor 130 is connected via one or more router devices 122 toradio access network (RAN) 102, which may be required for a 3G, 4G or a5G radio network. The service conductor 130 is connected via a routerdevice 124 to one or more alternative access vendors 104. Thesealternative access vendors 104 control all or a portion of the backhaulcapacity of the network with capability to expand or contract as needed.The service conductor 130 is also connected via one or more routerdevices 126 to the core network 106. On a 4G network, this core networkconnection might include connectivity to the Home Subscriber Server(HSS) or the Packet Gateway (PGW). On a 5G network, the core networkincludes the Access and Mobility Management Function (AMF) and the UserPlane Function (UPF), which may be configured to provide informationabout the network to the service conductor 130 deployed in a 5G network.The service conductor 130 as shown in FIG. 1 also has connectivity toPublic Cloud 108 via one or more router devices 128.

As further illustrated in FIG. 1, the service conductor 130 containsmultiple modules. The modules may be software applications, routines, orcomponents stored in a computer readable memory. All modules may beexecuted on the same processor, by multiple processors on the sameserver, or may be executed by multiple processors and multiple serversin a network configuration. Those with skill in the art will recognizethat all or a portion of the modules may be combined with each other.

The service conductor 130 contains a customer interface module 160,which accepts input from customers or customer applications wanting touse one or more of the network resources. The input may be acceptedelectronically from the customer via use of an application programminginterface (API) or accepted via a human interface. Besides the resourceneeded, this input from customer interface module 160 may include SLAsfor the various customers which are managed by the SLA engine 178. TheSLA for a customer may include price parameter data and may furthercontain payment commitments such as maximum price willing to be paid bythe customer per unit of a particular resource, per unit of time, ormaximum price in total for all resources, as well as other guidance. Thecustomer interface module 160 also may receive other input, such as aduration of need or allowable latency.

The service conductor 130 may further include a demand function 164relating to network resource utilization, particularly for edgeresources of the network. The demand function 164 aggregates resourcerequests and associated willingness to pay from all customers for suchservices and collates the requests across a time domain to generate ademand function across a time domain. The demand function 164 then sendsthe aggregated demand across time domain to the real-time pricingresolution function 132 as one of the two inputs. Another input of thereal-time pricing resolution function 132 comes from the supply function174 where it takes inputs from the assets catalog 180 and from the SLAengine 178 as well as the cost engine 166. Also illustrated is a supplyfunction 174 which takes input from assets catalog 180 and from the SLAengine 178 and sends output to a real-time pricing resolution function.The supply function 174 determines, using any of several resourceallocation models, the most cost effective way to meet different levelsof resource requirements at any given time. The supply function 174accepts inputs from the asset catalog 180, SLA engine 178, and costengine 166 and sends output to the real-time pricing resolution function132. The real-time pricing resolution function performs real-time pricedetermination based on previously described inputs from the demandfunction 164 and the supply function 174. After the price is resolved,the service orchestrator module 182 coordinates with the networkresources (such as radio access network 102 alternative access vendor104 core network 106 and Public Cloud 108) to fulfill and executeservice requests. The charging function134 charges customers forservices that have been fulfilled. In some embodiments the chargingfunction may connect back to customer interface module 160 in order togive immediate electronic presentation of the charge or it may simplysend output to the operator's billing system.

Also illustrated is the pricing forecast engine 176 of the serviceconductor 130, which may accept input from the cost engine 166 whichprovides, among other things, a cost floor. The pricing forecast engine176 may, in some embodiments, accept input from a real-time pricingresolution function. Artificial intelligence and machine learningtechniques may also be used in this module to predict, in advance,customer needs and/or pricing predictions which are then provided to thecustomer interface module 160 regarding the predicted price of theresource at the time the resource is needed. The predicted price may bebased upon the time of day, based upon the day of the week, based uponthe availability of one or more network resources, based upon theaggregate demand of resources at the time, or based upon the cost of thenetwork resource demanded, as well as each customer's upper price limit.The pricing forecast engine may, for example, discount or add a premiumto the cost based upon the time of day, the anticipated demand by othercustomers, or a variety of other factors. For example, a customer demandfor low latency processing during an operator's highest-demand timeperiods would be expected to require a higher price for the third partycustomer using the service than the same demand during off hours, whichmight be priced at a discount. The history or “reputation” of aparticular customer may also be considered if a particular customerconsistently underestimates or consistently overestimates the amount ofresources required. In a similar fashion, the history of a particularnetwork resource usage may also be considered if a plurality ofcustomers have a tendency to consistently overestimate or underestimateneed for that particular resource. In one embodiment, the pricingforecast is provided to the customer via the customer interface module160 for approval prior to services being used by the customer.

FIG. 2 illustrates a block diagram showing the service conductor 130 ina 5G wireless network. It should be noted that this represents anexemplary embodiment, and the service conductor 130 is not limited touse in a 5G network. In this 5G embodiment, the service conductor 130 isconnected to the access and mobility management function (AMF) 250 toobtain mobility and session information, especially mobility informationfor the user equipment (UE) 282. The policy control function (PCF) 270and the session management function (SMF) 260, as well as theapplication function (AF) 280, can also be accessed by the serviceconductor 130 through the AMF 250. The 3GPP elements of the networkslice selection services function module (NSSF) 210, the authenticationserver function (AUSF) 220 and the unified data management module (UDM)230 are also connected to the AMF 250. In the 5G embodiment of thisinvention, the service conductor 130 may also be connected to the userplane function (UPF) 292. This connection can be used by serviceconductor 130 to gain access to the data network (DN) 296. Also shown onthe diagram is the radio access network (RAN) 102 which connects the UE282 to the 5G network.

In a 4G cellular network embodiment of this invention, not shown, theservice conductor 130 may manage the resources of a 4G network byconnecting to the Home Subscriber Server (HSS), where the serviceconductor 130 obtains UE mobility information. The service conductor 130may also have a connection to the Packet Data Network Gateway (PGW)element where the service conductor 130 can gain access to the 4G datanetwork.

Moving next to FIG. 3, a block diagram of the service conductor 130 isillustrated, showing various components thereof. In this exemplaryembodiment, the service conductor 130 contains a controller 302, and thecontroller contains one or more processors 306 an input/output (I/O)controller 308 and a memory 304. The components of the controller 302may be interconnected via an address/data bus or other means. Thecomponents may all be contained in one service conductor 130 (e.g., inone server operating as the service conductor 130) or may be distributedin a networked fashion across multiple computing devices or systems(e.g., distributed across multiple servers interconnected and configuredto operate collectively as the service conductor 130). Although FIG. 3depicts only one processor 306, the controller 302 may include multipleprocessors 306 in some embodiments. The memory 304 may comprise randomaccess memory (RAM) and/or nonvolatile memory such as NVRAM, read onlymemory (ROM), flash memory, electrically erasable programmable ROM(EEPROM), or magnetic media such as a hard drive. The processor 306 maycomprise one or more microprocessors including a graphics or floatingpoint processor. The controller 302 also includes the input/outputcontroller 308. The input/output controller 308 is connected to thecommunication interface 312. The communication interface 312 isconnected to external elements such as the access and mobilitymanagement function 250 or a user plane function 292 element of a 5Gnetwork or the communication interface 312 may connect to the HSS or PGWof a 4G network.

The memory 304 contains non-transitory computer readable executableinstructions, which, when executed by a processor support at least oneof four modes of network resource management. These four modes includeMode 1 (block 310), in which the network resources are assigned on aone-to-one exclusive basis to customers. Mode 1 implies that if aresource is allocated to a customer, it cannot be used by anothercustomer even if the resource is not being fully utilized. Mode 2 (block320) oversubscribes network resource allocation from a shared pool ofresources with an allocation cap and a priority assigned to eachcustomer. Mode 3 (block 330) also oversubscribes network resources froma shared pool with a priority assigned to each customer, may use anaggregate customer resource cap, but with no allocation caps to eachcustomer. Mode 4 (block 340) is a hybrid of Modes 1, 2 and 3. In oneembodiment, Mode 1 would be appropriate for a group of customers payingfor a premium level service level agreement. Mode 2 might be used inanother embodiment where all customers are charged equivalently and theoperator needs to tightly manage resources. Mode 4 might be used tosupport both a subset of premium customers that have guaranteed serviceagreements (and assigned resources similar to Mode 1) and a simultaneousset of customers at the same operator at standard rates with resourcesassigned to the remainder of the resources similar to Mode 2 or Mode 3.A portion of the memory 304 may also be required and referenced by aprocessor 306.

FIG. 4 illustrates a flow diagram of an exemplary resource allocationmethod 400 to implement the dynamic real-time model to allocate networkresources. The resource allocation method 400 may be implemented by theservice conductor 130 described above to control the allocation oflimited network resources (particularly edge resources) in a wirelesstelecommunications network, as described above. In various embodiments,parts of the resource allocation method 400 may be implemented byvarious modules of the service conductor 130 or by such modules invarious combinations. To ensure the allocation of resources remainsoptimal as demand and network conditions change over time, the resourceallocation method 400 (or parts thereof) may be repeated on an ongoingbasis or on a periodic basis with short intervals between reallocation(e.g., every thirty seconds or every five minutes).

In block 410, the service conductor 130 obtains price parameter data.Price parameter profile data contained in customer SLAs, may be obtainedon a one-time basis for each customer, or updated periodically. The SLAmight indicate the total cost willing to be paid by each customer, theprice per service for a particular service for each customer, the priceper unit of time for the service, or a location associated with thedemand from the customer. This pricing data might also come from inputsfrom an API connecting to the customer interface module 160 portion ofthe service conductor 130 or from the customer SLA containing a priceprofile or price parameter data which may further indicate a customerpayment commitment. The commitment to pay for a network resource may bebased on maximum price per unit of resource, a payment commitment perunit of time, or payment commitment for a total charge. The pricing datamay also come from a cost engine used to determine a price floor where amultiplier is further applied by the operator (e.g., a multiplier of1.15 to generate a fifteen percent margin). The pricing data mayalternately be determined from a pricing forecast engine that usesartificial intelligence and machine learning to forecast prices, whichmay be approved in real-time by the customer, or from a combination ofsources. The pricing forecast engine may use historical pricing andresource usage data to predict one or more of the following: expecteddemand for one or more network resources, expected network resourceavailability, or optimal pricing for market-clearing network resourceallocation.

In block 420, the service conductor 130 then determines the level ofnetwork resources demanded by each customer and an estimated period oftime the resource is needed. Such network resource demand may beprice-dependent (e.g., a demand profile), as well as being dependentupon service parameters (e.g., latency, reliability, or peak usage). Anetwork resource demand profile may thus be generated to indicate types,locations, and parameters (e.g., time, service parameters, total usage)associated with network resource demand, particularly for edge resourcesthat may be more location-dependent. For example, an application thatcontrols vehicle braking may demand nearby resources with the lowestlatency and highest network usage. In a network limited by throughput ofsatellite links, an application may request resources on the applicationside of the satellite link or may request less costly (but slower)storage in another part of the network. In another example, a falldetection sensor for a person requires low latency and also relativelylow network resources. In a third example, in an agriculturalenvironment, farm equipment operating in the field and utilizing machinevision to identify plant types for appropriate action, may require lowlatency as well as relatively heavy usage from network resources.

In block 430 of this method, the resources from particular customers areaggregated by a demand function 164 in order to determine the totalresource demand on the network. This aggregation can be collated acrossa time domain or other parameters, such as location. This aggregatenetwork resource demand function indicates aggregate demand for one ormore types of network resources by price and at least one otherparameter, such as time, quality, or location. In block 440, the serviceconductor 130 determines the resource supply. This may be from supplyfunction 174, which may use the assets catalog 180, cost engine 166, aswell as one of the network resource models 310, 320, 330, or 340. Insome embodiments, the network resource supply function may be determinedbased upon real-time availability data regarding the network resources,which may include information concerning resource utilization oroperating status (e.g., resource operating health, such as whether anyerror conditions are limiting or preventing operation of particularnetwork resources).

In block 450, network resources are allocated to the customers basedupon the aggregate demand and the aggregate supply. The network resourcemodels 310, 320, 330, or 340 are used by the service conductor 130 todetermine the most economical and efficient network resources in block440 to meet the aggregate customer resource demand. The supply functionalso feeds information to a real-time pricing resolution function 132,which resolves pricing based upon supply and aggregate demand in a dualauction fashion. The real-time pricing resolution function outputs to acharging function. The charging function charges customers when servicesare fulfilled. In some embodiments, this function may transmit data tothe customer interface module 160. In some embodiments, specific networkresources are assigned to particular customers. When the resources areassigned for use, the transaction may be recorded in a transactionrepository 172, which may in some embodiments also contain a “reputationscore” change for the customer, and the assets catalog 180 may also thenbe updated with the information regarding the resource being used.Alternatively, actual resource use levels by the customers may bemonitored and recorded.

In block 460, the network resources are controlled by a serviceorchestrator module 182 of the service conductor 130 according to themodel selected (such as models 310, 320, 330, 340) and according to eachcustomer's determined resource allocation. The service orchestrator 130coordinates with various elements (which may include alternative accessvendors 104, core network 106 elements, and Public Cloud 108 elements)to provide service and fulfill the customer demand. After determining,an allocation of the network resources based upon the aggregate networkresource demand function and the network resource supply function, thereal-time pricing resolution function further adjusts the allocation inblock 470 in response to changes based upon current network resource useor changes based upon availability of one or more network resources.This method 400 monitors the network at block 480 for changes in demandor changes in network resource supply. If a change has occurred ineither supply (such as a network resource going out of service) or achange has occurred to demand such as when more applications now needthe network resource, the method 400 loops from 480 back to block 420.

In some embodiments, the service conductor 130 provides a pricingprediction for each customer based upon the level of network resourcesdemanded, the customer service level agreement, the supply of theresource, an aggregate resources demanded, and the duration of need.This prediction may be presented to the customer via customer interfacemodule 160. The pricing prediction may be adjusted based upon actuallevels of network resources used by each of the customers to determinethe pricing prediction for each customer. Past history of the particularcustomer may also be considered in the pricing prediction, throughmachine learning or other means. Machine learning, for example, may beused to adjust for some customers that may consistently underestimate oroverestimate their resource need. Alternately, some resources mayconsistently be over or underestimated by all customers as may bedetermined by comparing the SLA engine 178 or the demand function 164 tothe transaction repository 172 on a per customer basis or on a perresource used basis.

Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andcomponents functionality presented as separate components in exampleconfigurations may be implemented as a combined structure or component.Similarly, functionality of structures and components presented as asingle component may be implemented as separate components. These andother variations, modifications, additions, and improvements fall withinthe scope of the subject matter herein.

As used herein, the term non-transitory computer-readable storage mediumis expressly defined to include any type of computer-readable storagedevice and/or storage disk and to exclude propagating signals and toexclude transmission media. As used herein, the term non-transitorymachine-readable medium is expressly defined to include any type ofmachine-readable storage device and/or storage disk and to excludepropagating signals and to exclude transmission media.

This detailed description is to be construed as exemplary only and doesnot describe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One could implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application. Uponreading this disclosure, those of skill in the art will appreciate stilladditional alternative structural and functional designs for systems andmethods according to the disclosed principles herein. Thus, whileparticular embodiments and applications have been illustrated anddescribed, it is to be understood that the disclosed embodiments are notlimited to the precise construction and components disclosed herein.Various modifications, changes and variations, which will be apparent tothose skilled in the art, may be made in the arrangement, operation anddetails of the techniques disclosed herein without departing from thespirit and scope defined in the appended claims.

What is claimed:
 1. A method to provide dynamic network resourcemanagement in a telecommunications network, comprising: obtaining, byone or more processors, price parameter data in a plurality of customerservice level agreements associated with a plurality of customers;determining, by the one or more processors, levels of network resourcesdemanded by each of the plurality of customers based upon currentnetwork resource use; generating, by the one or more processors, anaggregate network resource demand function based upon the priceparameter data and the levels of network resources demanded, theaggregate network resource demand function indicating types of networkresources demanded and locations associated with the demand; generating,by the one or more processors, a network resource supply function basedupon availability of one or more network resources in thetelecommunications network, wherein the network resource supply functionindicates the types of the network resources and locations associatedwith the network resources; determining, by the one or more processors,an allocation of the network resources based upon the aggregate networkresource demand function and the network resource supply function;controlling, by the one or more processors, the network resources toprovide services to the plurality of customers according to theallocation; and adjusting, by the one or more processors, the allocationin response to changes to current network resource use or changes to theavailability of the network resources.
 2. The method of claim 1, whereinat least a portion of the network resources are located at an edge ofthe telecommunications network.
 3. The method of claim 1, whereincontrolling the network resources according to the allocation includesassigning at least a portion of the network resources on an exclusivebasis to at least one of the customers.
 4. The method of claim 1,wherein controlling the network resources according to the allocationincludes oversubscribing at least a portion of the network resources tocustomers from a shared pool of resources, with the customers assigned apriority in the shared pool and with the shared pool of resources havingan aggregate customer resource cap.
 5. The method of claim 1, whereincontrolling the network resources according to the allocation includesoversubscribing at least a portion of the network resources assigned toa shared pool of resources, with each customer assigned a priority inthe shared pool and each customer assigned a resource cap.
 6. The methodof claim 1, further comprising: determining, by one or more processors,a duration of need for each network resource by each customer, whereinthe allocation of the network resources is further based upon theduration of need for each network and each customer.
 7. The method ofclaim 6, further comprising: providing, by the one or more processors, apricing prediction for each customer based upon the level of networkresources demanded, the customer service level agreement, a supply ofthe resource, an aggregate resources demand, and the duration of need;recording, in a repository, the levels of network resources demanded byeach customer; and adjusting, by the one or more processors, the pricingprediction based upon actual levels of network resources used by each ofthe plurality of customers to determine the pricing prediction for eachcustomer.
 8. The method of claim 7, wherein the service level agreementassociated with the customers include payment commitments for networkresources based upon the pricing predictions.
 9. The method of claim 8,wherein the payment commitment includes: committing based on a unit oftime, committing based on a unit of resource, or committing based ontotal charge.
 10. A non-transitory computer-readable storage mediumstoring executable instructions that, when executed by one or moreprocessors, cause the processors to: obtain price parameter data in aplurality of customer service level agreements associated with aplurality of customers; determine levels of network resources demandedby each of the plurality of customers based upon current networkresource use; generate an aggregate network resource demand functionbased upon the price parameter data, the levels of network resourcesdemanded, types of network resources demanded and locations associatedwith the demand; generate a network resource supply function based uponavailability of one or more network resources in a telecommunicationsnetwork, the network resource supply function indicating the types ofthe network resources and locations associated with the networkresources; determine an allocation of the network resources based uponthe aggregate network resource demand function and the network resourcesupply function; control the network resources to provide services tothe plurality of customers according to the allocation; and adjust theallocation in response to changes to current network resource use orchanges to the availability of the network resources.
 11. Thenon-transitory computer-readable storage medium of claim 10, whereincontrolling the network resources according to the allocation includesassigning at least a portion of the network resources on an exclusivebasis to at least one of the customers.
 12. The non-transitorycomputer-readable storage medium of claim 10, wherein controlling thenetwork resources according to the allocation includes oversubscribingat least a portion of the network resources to customers from a sharedpool of resources, with the customers assigned a priority in the sharedpool and with the shared pool of resources having an aggregate customerresource cap.
 13. The non-transitory computer-readable storage medium ofclaim 10, wherein controlling the network resources according to theallocation includes oversubscribing at least a portion of the networkresources assigned to a shared pool of resources, with each customerassigned a priority in the shared pool and each customer assigned aresource cap.
 14. The non-transitory computer-readable storage medium ofclaim 10, wherein controlling the network resources according to theallocation includes oversubscribing at least a portion of the networkresources assigned to a shared pool of resources, with each customerassigned a priority in the shared pool and each customer assigned aresource cap.
 15. The non-transitory computer-readable storage medium ofclaim 10, wherein the executable instructions further cause theprocessors to: determine a duration of need for each network resource byeach customer, wherein the allocation of the network resources isfurther based upon the duration of need for each network and eachcustomer.
 16. A system for providing dynamic network resource managementin a telecommunications network having at least a portion of the networkresources located at an edge of the telecommunications network,comprising: one or more processors; a program memory storing executableinstructions that, when executed by the one or more processors, causethe system to: obtain price parameter data in a plurality of customerservice level agreements associated with a plurality of customers;determine levels of network resources demanded by each of the pluralityof customers based upon current network resource use; generate anaggregate network resource demand function based upon the priceparameter data, the levels of network resources demanded, types ofnetwork resources demanded and locations associated with the demand areindicated; generate a network resource supply function based uponavailability of one or more network resources in the telecommunicationsnetwork, the network resource supply function indicating the types ofthe network resources and locations associated with the networkresources; determine an allocation of the network resources based uponthe aggregate network resource demand function and the network resourcesupply function; control the network resources to provide services tothe plurality of customers according to the allocation; and adjust theallocation in response to changes to current network resource use orchanges to the availability of the network resources.
 17. The system ofclaim 16, wherein the executable instructions that cause the system tocontrol the network resources according to the allocation cause thesystem to assigning at least a portion of the network resources on anexclusive basis to customers.
 18. The system of claim 16, wherein theexecutable instructions that cause the system to control the networkresources according to the allocation cause the system to oversubscribeat least a portion of the network resources to customers from a sharedpool of resources, with the customers assigned a priority in the sharedpool and with the shared pool of resources having an aggregate customerresource cap.
 19. The system of claim 16 wherein the executableinstructions that cause the system to control the network resourcesaccording to the allocation cause the system to oversubscribe at least aportion of the network resources assigned to a shared pool of resources,with each customer assigned a priority in the shared pool and eachcustomer assigned a resource cap.
 20. The system of claim 19, whereinthe executable instructions further cause the system to: provide apricing prediction for each customer based upon the level of networkresources demanded, the customer service level agreements, the supply ofthe resource, an aggregate resources demanded, and a duration of need;record the levels of network resources demanded by each customer in arepository; and adjust the pricing prediction based upon actual levelsof network resources used by each of the plurality of customers todetermine the pricing prediction for each customer.